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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Label-free diagnosis across the thyroid nodule pathology spectrum using deep learning-enabled optical coherence

Woojin Lee¹, Soonyong Kwon¹, Hyeong Soo Nam¹

¹Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.

Biomedical Optics Express

|May 18, 2026

Summary

A new deep learning framework analyzes optical coherence tomography (OCT) images for thyroid nodule diagnosis. This AI tool accurately distinguishes cancerous from non-cancerous thyroid nodules, improving diagnostic capabilities.

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Area of Science:

Biomedical Imaging
Artificial Intelligence in Medicine
Pathology

Background:

Thyroid nodules are common, but differentiating malignant from benign types is challenging with current methods.
Traditional diagnosis involves invasive biopsies and lengthy histopathology, hindering real-time assessment.
Optical coherence tomography (OCT) offers non-invasive imaging, but its pathological interpretation is difficult.

Purpose of the Study:

To develop a deep learning (DL) framework for classifying thyroid nodule pathology using OCT images.
To enable accurate, real-time pathological assessment of thyroid nodules non-invasively.
To improve diagnostic accuracy for thyroid carcinoma subtypes and benign conditions.

Main Methods:

Acquired OCT datasets from seven pathological categories (5 carcinoma subtypes, benign, normal).
Utilized histology-matched OCT data for supervised deep learning model training.
Developed a DL framework for binary (carcinoma vs. non-carcinoma) and multi-class classification.
Visualized diagnostic predictions using color-coded overlays on OCT images.

Main Results:

Achieved 98.37% accuracy and 0.997 AUC for binary classification of carcinoma vs. non-carcinoma.
Demonstrated 93.66% overall accuracy for multi-class classification across seven categories.
Enabled coherent interpretation of tissue pathology through visualized diagnostic predictions.

Conclusions:

The combination of OCT and DL shows feasibility for enhanced thyroid pathology assessment.
This approach supports real-time and point-of-care diagnostic applications for thyroid nodules.
Further optimization can lead to improved clinical deployment for thyroid nodule diagnostics.